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Deep Learning Breakthrough in Machine Learning

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Karl Seiler
Karl Seiler

Machine Learning via Deep Learning approach. A summary and some examples.

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DEEP LEARNINGBreakthrough In Machine Learning Intro & Tour
Central Florida Machine Learning, Predictive Analytics and Automated Reasoning “Smart tech is here, now, and integral to your engineering.”
INTROS
Exploring and sharing How to… What’s new / hot Engineering Resources
Data Science Big Data Machine Learning Semantics Predictive Analytics Internet of Things Artificial Intelligence Language Processing
DEEP LEARNINGBreakthrough In Machine Learning Intro & Tour
Bill Gates, then Chairman, Microsoft “A breakthrough in machine learning would be worth ten Microsofts.”
Deep Learning Algos • Deep Boltzmann Machine (DBM) • Deep Belief Networks (DBN) • Convolutional Neural Network (CNN) • Stacked Auto-Encoders
HISTORY • Neural nets - big in the late 80’s - Despite a commonly-held belief, there have been numerous successful applications • Out of fashion in the 90’s • 2003 renewed interest in the problem of learning representations (as opposed to just learning simple classifiers) - LeChun • 2006-2007 traction via unsupervised training - Ng • Now “Deep Learning” has come to designate any learning method that can train a system with more than 2 or 3 non-linear hidden layers.
WHY NOW? • More and diverse data • More processing power • Algorithm advances and discoveries • GPUs
Andrew Ng Yann LeCun
EXAMPLES
Image Recognition
Image Recognition
Speech Recognition
Natural Language • Constituency parsing • Sentiment analysis • Information retrieval • Machine translation • Contextual entity linking
VIDEO
Andrew Ng: Deep Learning… https://www.youtube.com/watch?v=n1ViNeWhC24
Introduction to Deep Learning with Python https://www.youtube.com/watch?v=S75EdAcXHKk
TOOLS
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.
• Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. It provides parallelization with CPUs and GPUs. • Theano — An open source machine learning library for Python. • Deeplearning4j — An open source deep learning library written for Java. It provides parallelization with CPUs and GPUs. • OpenNN — An open source C++ library which implements deep neural networks and provides parallelization with CPUs. • NVIDIA cuDNN — A GPU-accelerated library of primitives for deep neural networks. • DeepLearnToolbox — A Matlab/Octave toolbox for deep learning. • convnetjs — A Javascript library for training deep learning models. It contains online demos. • Gensim — A toolkit for natural language processing implemented in the Python programming language. • Caffe — A deep learning framework. • Apache SINGA — A deep learning platform developed for scalability, usability and extensibility. • RNNLM — RNN language model open source. • RNNLMPara — Parallel RNN language model trainer open source. Other Tools
Karl Seiler | President karl@piviting.com Piviting.com @pivitguru SMARTER CHANGE

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Deep Learning Breakthrough in Machine Learning

  • 1. DEEP LEARNINGBreakthrough In Machine Learning Intro & Tour
  • 2. Central Florida Machine Learning, Predictive Analytics and Automated Reasoning “Smart tech is here, now, and integral to your engineering.”
  • 4. Exploring and sharing How to… What’s new / hot Engineering Resources
  • 5. Data Science Big Data Machine Learning Semantics Predictive Analytics Internet of Things Artificial Intelligence Language Processing
  • 6. DEEP LEARNINGBreakthrough In Machine Learning Intro & Tour
  • 7. Bill Gates, then Chairman, Microsoft “A breakthrough in machine learning would be worth ten Microsofts.”
  • 8.
  • 9.
  • 10.
  • 11. Deep Learning Algos • Deep Boltzmann Machine (DBM) • Deep Belief Networks (DBN) • Convolutional Neural Network (CNN) • Stacked Auto-Encoders
  • 12. HISTORY • Neural nets - big in the late 80’s - Despite a commonly-held belief, there have been numerous successful applications • Out of fashion in the 90’s • 2003 renewed interest in the problem of learning representations (as opposed to just learning simple classifiers) - LeChun • 2006-2007 traction via unsupervised training - Ng • Now “Deep Learning” has come to designate any learning method that can train a system with more than 2 or 3 non-linear hidden layers.
  • 13. WHY NOW? • More and diverse data • More processing power • Algorithm advances and discoveries • GPUs
  • 19. Natural Language • Constituency parsing • Sentiment analysis • Information retrieval • Machine translation • Contextual entity linking
  • 20. VIDEO
  • 22. Introduction to Deep Learning with Python https://www.youtube.com/watch?v=S75EdAcXHKk
  • 23. TOOLS
  • 24. TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
  • 25. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.
  • 26. • Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. It provides parallelization with CPUs and GPUs. • Theano — An open source machine learning library for Python. • Deeplearning4j — An open source deep learning library written for Java. It provides parallelization with CPUs and GPUs. • OpenNN — An open source C++ library which implements deep neural networks and provides parallelization with CPUs. • NVIDIA cuDNN — A GPU-accelerated library of primitives for deep neural networks. • DeepLearnToolbox — A Matlab/Octave toolbox for deep learning. • convnetjs — A Javascript library for training deep learning models. It contains online demos. • Gensim — A toolkit for natural language processing implemented in the Python programming language. • Caffe — A deep learning framework. • Apache SINGA — A deep learning platform developed for scalability, usability and extensibility. • RNNLM — RNN language model open source. • RNNLMPara — Parallel RNN language model trainer open source. Other Tools
  • 27. Karl Seiler | President karl@piviting.com Piviting.com @pivitguru SMARTER CHANGE